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极端降水影响下粤港澳大湾区城市洪涝人口暴露度预估OA

Estimation of population exposure to urban flooding in the Guangdong-Hong Kong-Macao Greater Bay Area under extreme rainfall

中文摘要英文摘要

通过构建基于多维条件概率模型的城市洪涝人口暴露度评估方法,以粤港澳大湾区珠三角九市为研究对象,开展了 2030-2100年中度温室气体排放情景(SSP2-4.5)的气候变化和不同降水强度情景下,受城市洪涝威胁的人口暴露度的预估分析.结果表明,在中等强降水情景下,未来粤港澳大湾区城市受洪涝威胁的最大人口暴露度将超过6000人/km2;在极端暴雨情景下,受城市洪涝威胁的最大人口暴露度可达到8000~12 000人/km2.此外,在极端暴雨影响下,未来广州西部、广州与佛山交界区域、深圳大部分区域和东莞与深圳交界区域将始终是粤港澳大湾区受城市洪涝威胁的人口暴露高风险地区,平均人口暴露度高达3500人/km2以上.以上研究结果可为制定未来粤港澳大湾区综合防洪策略和提升城市洪涝安全韧性水平提供科学依据.

This study develops a method for assessing population exposure to urban flooding based on a multidimensional conditional probability model,focusing on the nine cities of the Pearl River Delta in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA).The estimation of population exposure was conducted under moderate greenhouse gas emission scenarios(SSP2-4.5)and various rainfall intensities for the period 2030-2100.The results indicate that under moderate heavy rainfall scenarios,the maximum population density exposure to urban flooding in the GBA will exceed 6000 people/km2.Under extreme rainfall scenarios,the maximum population exposure to urban flooding could reach 8000 to 12 000 people/km2.Furthermore,under extreme rainfall impacts,the western part of Guangzhou,the Guangzhou-Foshan border area,most parts of Shenzhen,and the Dongguan-Shenzhen border area will consistently be high-risk zones for urban flood exposure in the GBA,with an average population exposure exceeding 3500 people/km2.These findings provide scientific support for developing comprehensive flood prevention strategies and enhancing urban flood resilience in the GBA.

刘智勇;陈晓宏;林凯荣;涂新军;赵铜铁钢;佘贞燕

中山大学土木工程学院水资源与环境研究中心,510275,广州||南方海洋科学与工程广东省实验室,519082,珠海南方海洋科学与工程广东省实验室,519082,珠海||中山大学海洋工程与技术学院河口海岸研究所,519082,珠海

水利科学

Copula函数强降雨洪水暴露SSP2-4.5气候变化粤港澳大湾区

Copulaheavy rainfallexposure to floodingSSP2-4.5climate changeGuangdong-Hong Kong-Macao Greater Bay Area

《中国水利》 2024 (013)

34-38 / 5

国家重点研发计划项目(2021YFC3001000);国家自然科学基金项目(52179031);广东省杰出青年基金项目(2023B1515020116);广东省基础与应用基础研究基金项目(2023B1515040028).

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